library(magrittr)
library(tidyverse)
library(Seurat)
library(readxl)
library(cowplot)
library(colorblindr)
library(viridis)
library(progeny)
library(destiny)
coi <- params$cell_type_super
cell_sort <- params$cell_sort
cell_type_major <- params$cell_type_major
louvain_resolution <- params$louvain_resolution
louvain_cluster <- params$louvain_cluster
### load all data ---------------------------------
source("_src/global_vars.R")
# seu_obj <- read_rds(paste0("/work/shah/isabl_data_lake/analyses/16/52/1652/celltypes/", coi, "_processed.rds"))
seu_obj <- read_rds(paste0("/work/shah/uhlitzf/data/SPECTRUM/freeze/v6/outs_pre/", coi, "_seurat_", louvain_resolution, ".rds"))
# seu_obj <- read_rds(paste0("/work/shah/uhlitzf/data/SPECTRUM/freeze/v6/", coi, "_highqc.rds"))
myfeatures <- c("umapharmony_1", "umapharmony_2", "sample", louvain_cluster, "doublet", "nCount_RNA", "nFeature_RNA", "percent.mt", "doublet_score")
plot_data_wrapper <- function(cluster_res) {
cluster_res <- enquo(cluster_res)
as_tibble(FetchData(seu_obj, myfeatures)) %>%
left_join(meta_tbl, by = "sample") %>%
rename(cluster = !!cluster_res) %>%
mutate(cluster = as.character(cluster),
tumor_supersite = ordered(tumor_supersite, levels = names(clrs$tumor_supersite)))
}
plot_data <- plot_data_wrapper(louvain_cluster)
helper_f <- function(x) ifelse(is.na(x), "", x)
markers_v6_super[[coi]] %>%
group_by(subtype) %>%
mutate(rank = row_number(gene)) %>%
spread(subtype, gene) %>%
mutate_all(.funs = helper_f) %>%
formattable::formattable()
| rank | CD4.T.CXCL13 | CD4.T.naive | CD4.T.reg | CD8.T.CXCL13 | CD8.T.cytotoxic | CD8.T.ISG | Cycling.T.NK | dissociated | NK.CD56 | NK.cytotoxic | NK.naive |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | CD4 | CCR7 | CD4 | CCL3 | CCL4 | IFI6 | ASPM | BTG1 | AREG | FCGR3A | CCR6 |
| 2 | CD40LG | CD4 | FOXP3 | CCL4L2 | CCL5 | IFIT1 | CENPF | DNAJB1 | FCER1G | FGFBP2 | IL4I1 |
| 3 | CTLA4 | CD40LG | IL2RA | CD8A | CD8A | IFIT2 | HIST1H4C | DUSP1 | GNLY | KLRD1 | IL7R |
| 4 | CXCL13 | IL7R | TNFRSF4 | CRTAM | CD8B | IFIT3 | HMGB2 | EGR1 | KLRC1 | KLRF1 | KLRB1 |
| 5 | FKBP5 | KLF2 | TRAC | CXCL13 | CRTAM | ISG15 | MKI67 | FOS | KRT81 | PRF1 | LST1 |
| 6 | IL6ST | LTB | FABP5 | CST7 | MX1 | STMN1 | FOSB | TRDC | SPON2 | LTB | |
| 7 | ITM2A | TCF7 | GZMB | DTHD1 | MX2 | TOP2A | HSPA1A | TYROBP | TNFSF13B | ||
| 8 | MAF | TPT1 | HAVCR2 | GZMA | RSAD2 | TUBA1B | HSPA1B | XCL1 | |||
| 9 | NMB | IFNG | GZMH | TUBB | HSPA6 | XCL2 | |||||
| 10 | NR3C1 | LAG3 | GZMK | TYMS | JUN | ||||||
| 11 | PDCD1 | MIR155HG | GZMM | JUNB | |||||||
| 12 | TNFRSF4 | PHLDA1 | HLA-DPB1 | KLF2 | |||||||
| 13 | TOX2 | PTMS | ITM2C | MT1E | |||||||
| 14 | TSHZ2 | RBPJ | KLRG1 | MT1X | |||||||
| 15 | TNFRSF9 | TRGC2 |
# marker_tbl <- read_tsv(paste0("/work/shah/isabl_data_lake/analyses/16/52/1652/celltypes/", coi, "_markers.tsv")) %>%
# filter(resolution == louvain_resolution)
marker_tbl <- read_tsv(paste0("/work/shah/uhlitzf/data/SPECTRUM/freeze/v6/outs_pre/", coi, "_markers_", louvain_resolution, ".tsv"))
# marker_tbl <- read_tsv(paste0("/work/shah/uhlitzf/data/SPECTRUM/freeze/v6/", coi, "_highqc_markers_02.tsv"))
## Hypergeometric test --------------------------------------
test_set <- marker_tbl %>%
group_by(cluster) %>%
filter(!str_detect(gene, "^RPS|^RPL")) %>%
slice(1:50) %>%
mutate(k = length(cluster)) %>%
ungroup %>%
select(cluster, gene, k) %>%
mutate(join_helper = 1) %>%
group_by(cluster, join_helper, k) %>%
nest(test_set = gene)
markers_doub_tbl <- markers_v6 %>%
enframe("subtype", "gene") %>%
filter(!(subtype %in% unique(c(coi, cell_type_major)))) %>%
unnest(gene) %>%
group_by(gene) %>%
filter(length(gene) == 1) %>%
mutate(subtype = paste0("doublet.", subtype)) %>%
bind_rows(tibble(subtype = "Mito.high", gene = grep("^MT-", rownames(seu_obj), value = T)))
ref_set <- markers_v6_super[[coi]] %>%
bind_rows(markers_doub_tbl) %>%
group_by(subtype) %>%
mutate(m = length(gene),
n = length(rownames(seu_obj))-m,
join_helper = 1) %>%
group_by(subtype, m, n, join_helper) %>%
nest(ref_set = gene)
hyper_tbl <- test_set %>%
left_join(ref_set, by = "join_helper") %>%
group_by(cluster, subtype, m, n, k) %>%
do(q = length(intersect(unlist(.$ref_set), unlist(.$test_set)))) %>%
mutate(pval = 1-phyper(q = q, m = m, n = n, k = k)) %>%
ungroup %>%
mutate(qval = p.adjust(pval, "BH"),
sig = qval < 0.01)
# hyper_tbl %>%
# group_by(subtype) %>%
# filter(any(qval < 0.01)) %>%
# ggplot(aes(subtype, -log10(qval), fill = sig)) +
# geom_bar(stat = "identity") +
# facet_wrap(~cluster) +
# coord_flip()
low_rank <- str_detect(unique(hyper_tbl$subtype), "doublet|dissociated")
subtype_lvl <- c(sort(unique(hyper_tbl$subtype)[!low_rank]), sort(unique(hyper_tbl$subtype)[low_rank]))
cluster_label_tbl <- hyper_tbl %>%
mutate(subtype = ordered(subtype, levels = subtype_lvl)) %>%
arrange(qval, subtype) %>%
group_by(cluster) %>%
slice(1) %>%
mutate(subtype = ifelse(sig, as.character(subtype), paste0("unknown_", cluster))) %>%
select(cluster, cluster_label = subtype) %>%
ungroup %>%
mutate(cluster_label = make.unique(cluster_label, sep = "_"))
seu_obj$cluster_label <- unname(deframe(cluster_label_tbl)[as.character(unlist(seu_obj[[paste0("RNA_snn_res.", louvain_resolution)]]))])
plot_data$cluster_label <- seu_obj$cluster_label
cluster_n_tbl <- seu_obj$cluster_label %>%
table() %>%
enframe("cluster_label", "cluster_n") %>%
mutate(cluster_nrel = cluster_n/sum(cluster_n))
marker_sheet <- marker_tbl %>%
left_join(cluster_label_tbl, by = "cluster") %>%
group_by(cluster_label) %>%
filter(!str_detect(gene, "^RPS|^RPL")) %>%
slice(1:50) %>%
mutate(rank = row_number(-avg_logFC)) %>%
select(cluster_label, gene, rank) %>%
ungroup %>%
mutate(cluster_label = ordered(cluster_label, levels = unique(c(names(clrs$cluster_label[[coi]]), sort(cluster_label))))) %>%
spread(cluster_label, gene) %>%
mutate_all(.funs = helper_f)
marker_tbl_annotated <- marker_tbl %>%
left_join(cluster_label_tbl, by = "cluster") %>%
left_join(cluster_n_tbl, by = "cluster_label") %>%
select(-cluster, -resolution) %>%
mutate(cluster_label = ordered(cluster_label, levels = unique(c(names(clrs$cluster_label[[coi]]), sort(cluster_label))))) %>%
arrange(cluster_label, -avg_logFC, p_val_adj)
write_tsv(marker_sheet, paste0("/work/shah/uhlitzf/data/SPECTRUM/freeze/v6/", coi, "_marker_sheet.tsv"))
write_tsv(marker_sheet, paste0("/work/shah/uhlitzf/data/SPECTRUM/freeze/v6/supplementary_tables/", coi, "_marker_sheet.tsv"))
write_tsv(marker_tbl_annotated, paste0("/work/shah/uhlitzf/data/SPECTRUM/freeze/v6/", coi, "_marker_table_annotated.tsv"))
write_tsv(marker_tbl_annotated, paste0("/work/shah/uhlitzf/data/SPECTRUM/freeze/v6/supplementary_tables/", coi, "_marker_table_annotated.tsv"))
formattable::formattable(marker_sheet)
| rank | CD4.T.naive | CD4.T.CXCL13 | CD4.T.reg | CD8.T.cytotoxic | CD8.T.ISG | CD8.T.CXCL13 | NK.naive | NK.CD56 | NK.cytotoxic | Cycling.T.NK | Cycling.T.NK_1 | dissociated | dissociated_1 | dissociated_2 | doublet.Fibroblast | doublet.Fibroblast_1 | doublet.Monocyte | doublet.Plasma.cell |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | IL7R | CXCL13 | TNFRSF4 | GZMK | IFIT3 | CXCL13 | KLRB1 | GNLY | FGFBP2 | STMN1 | CENPF | CCL4L2 | HSPA1A | KLF2 | DCN | IGFBP5 | HLA-DRA | SOX4 |
| 2 | CCR7 | NMB | IL2RA | CD8A | ISG15 | GZMB | IL4I1 | TYROBP | FCGR3A | TUBA1B | ASPM | CCL4 | HSPA1B | CCR7 | IGFBP5 | TAGLN | CST3 | PTCRA |
| 3 | KLF2 | NR3C1 | FOXP3 | CD8B | MX1 | CCL4L2 | IL7R | AREG | SPON2 | MKI67 | MKI67 | IFNG | MT1X | JUNB | RBP1 | ADIRF | CXCL8 | MAL |
| 4 | EEF1B2 | MAF | CTLA4 | ITM2C | IFIT1 | MIR155HG | LTB | KLRC1 | PRF1 | HIST1H4C | HMGB2 | FOS | DNAJB1 | FOS | C7 | DCN | SPP1 | MZB1 |
| 5 | TPT1 | FKBP5 | LTB | GZMH | RSAD2 | TNFRSF9 | LST1 | FCER1G | KLRF1 | TUBB | TOP2A | FOSB | MT1E | DUSP1 | MEG3 | IGFBP7 | S100A9 | DNTT |
| 6 | EEF1A1 | IL6ST | RTKN2 | CCL5 | IFIT2 | HAVCR2 | TNFSF13B | TRDC | GNLY | TOP2A | UBE2C | TNF | HSPA6 | SELL | CALD1 | SPARCL1 | S100A8 | TFDP2 |
| 7 | MAL | ITM2A | BATF | TRGC2 | IFI6 | RBPJ | CCR6 | XCL1 | KLRD1 | TYMS | CCNB1 | JUN | HSP90AA1 | IL7R | IGFBP4 | CALD1 | HLA-DQA1 | CD1E |
| 8 | TCF7 | TSHZ2 | TNFRSF18 | KLRG1 | MX2 | LAG3 | CTSH | KLRD1 | NKG7 | CENPF | UBE2S | EGR1 | HSPH1 | AREG | RARRES2 | MGP | CD74 | STMN1 |
| 9 | CD40LG | CTLA4 | SAT1 | GZMA | IFI44L | IFNG | AQP3 | KRT81 | CX3CR1 | HMGB2 | PTTG1 | CCL3L1 | HSPE1 | EEF1A1 | NR2F2 | C11orf96 | LYZ | ARPP21 |
| 10 | SELL | CD40LG | TBC1D4 | CCL4 | ISG20 | CCL3 | CCL20 | XCL2 | GZMB | ASPM | TPX2 | NFKBID | HSPD1 | CD69 | SELENOP | MYL9 | FTL | AC084033.3 |
| 11 | GPR183 | PDCD1 | TIGIT | CRTAM | HERC5 | PTMS | NFKBIA | IGFBP2 | PLAC8 | NUSAP1 | STMN1 | CD69 | HSPB1 | BTG2 | MDK | TIMP3 | APOE | CDK6 |
| 12 | LDHB | CD4 | GADD45A | CST7 | SAMD9L | CD8A | RORA | CLIC3 | CLIC3 | PCLAF | KPNA2 | NR4A2 | HSP90AB1 | GPR183 | SOX4 | MDK | HLA-DQB1 | MAP1A |
| 13 | NOSIP | LIMS1 | TNFRSF1B | GZMM | OAS1 | CRTAM | TNFRSF25 | IL2RB | PLEK | HMGN2 | CENPE | AC020916.1 | DNAJA1 | PIK3IP1 | ADIRF | TPM2 | MARCKS | AC011893.1 |
| 14 | SNHG8 | TNFRSF4 | PMAIP1 | HLA-DPB1 | TNFSF10 | PHLDA1 | CEBPD | CEBPD | TYROBP | H2AFZ | CKS2 | EGR2 | JUN | CD55 | MGP | NR2F2 | AIF1 | GLUL |
| 15 | PABPC1 | RNF19A | UGP2 | DTHD1 | STAT1 | FABP5 | TNFAIP3 | KRT86 | PTGDS | PCNA | TUBA1B | DUSP1 | CACYBP | DNAJB1 | EGR1 | FBLN1 | C1QB | ADA |
| 16 | NOP53 | CORO1B | IKZF2 | PPP1R14B | EIF2AK2 | TIGIT | NCR3 | TXK | EFHD2 | HIST1H1B | HMMR | TNFSF9 | HSPA8 | FKBP5 | STAR | IGF1 | FTH1 | AL357060.1 |
| 17 | LEF1 | RBPJ | TNFRSF9 | CD3G | OAS3 | KRT86 | SLC4A10 | CTSW | FCER1G | DUT | DLGAP5 | KLF6 | FKBP4 | TSC22D3 | SFRP4 | RAMP1 | BASP1 | GRASP |
| 18 | EIF3E | CPM | ICOS | THEMIS | SAMD9 | JAML | TMIGD2 | KLRB1 | CST7 | CLSPN | CCNB2 | TNFAIP3 | CHORDC1 | RACK1 | CLU | SELENOP | C1QA | CD1B |
| 19 | LTB | ZBED2 | LINC01943 | DUSP2 | XAF1 | CCL5 | DPP4 | MATK | GZMH | SMC4 | TUBB4B | DUSP2 | RGS2 | LDHB | ADAMTS1 | RARRES2 | C15orf48 | MIR181A1HG |
| 20 | RACK1 | AC004585.1 | IL32 | CD3D | GBP1 | LINC01871 | TPT1 | CCL3 | ADGRG1 | ATAD2 | NUSAP1 | BTG2 | FOS | SARAF | TIMP2 | LUM | FN1 | CCDC26 |
| 21 | NACA | TOX2 | SOX4 | LYAR | EPSTI1 | CXCR6 | MYBL1 | CD7 | CCL3 | SMC2 | BIRC5 | IER2 | ANXA1 | CXCR4 | TCEAL4 | IGFBP6 | MNDA | JCHAIN |
| 22 | UBA52 | DUSP4 | ARID5B | TC2N | IFI44 | TNIP3 | S100A4 | NKG7 | HOPX | TPX2 | HMGN2 | PPP1R15A | DNAJB4 | PLAC8 | C1R | CARMN | G0S2 | VIPR2 |
| 23 | TOMM7 | AHI1 | CD27 | EOMES | PLSCR1 | PDCD1 | CD40LG | CD63 | IGFBP7 | TMPO | ARL6IP1 | NR4A1 | PPP1R15A | EEF1B2 | WFDC2 | IGFBP4 | APOC1 | ID1 |
| 24 | SOCS3 | ICA1 | BIRC3 | CXCR6 | IFI35 | HLA-DRB1 | SPOCK2 | HOPX | ZEB2 | HELLS | CDC20 | NFKBIA | ZFAND2A | TCF7 | FHL2 | DST | SOD2 | SOCS2 |
| 25 | JUNB | ARID5B | LAYN | HLA-DPA1 | USP18 | GAPDH | LINC01871 | TMIGD2 | PRSS23 | UBE2C | CDKN3 | JUND | FOSB | TPT1 | IFITM3 | CAV1 | NPC2 | RCAN1 |
| 26 | SERINC5 | CD84 | CORO1B | HLA-DRB1 | OASL | FAM3C | ERN1 | CMC1 | AKR1C3 | NASP | SMC4 | GADD45B | DNAJA4 | FOSB | PEG3 | ID4 | LST1 | CLDN5 |
| 27 | TMEM123 | CCDC50 | TYMP | SLF1 | MT2A | CTLA4 | JAML | TNFRSF18 | CD247 | DEK | CKS1B | TSC22D3 | DUSP1 | NOSIP | C1S | NUPR1 | GSN | CASC15 |
| 28 | EEF2 | IGFL2 | DUSP4 | APOBEC3G | HELZ2 | SPRY1 | FKBP11 | KLRC2 | MYBL1 | RRM2 | KIF20B | TAGAP | TSPYL2 | SC5D | LUM | CSRP2 | MS4A6A | PFKFB2 |
| 29 | FXYD5 | RGS1 | CD4 | PPP2R5C | TRIM22 | CCND2 | IFNGR1 | SRGAP3 | AREG | CKS1B | GTSE1 | ZFP36 | SERPINH1 | NACA | SERPINF1 | CDKN1C | IL1B | GALNT2 |
| 30 | TSHZ2 | BATF | ENTPD1 | KIAA1551 | CMPK2 | CD63 | MGAT4A | GSTP1 | C1orf21 | KNL1 | TUBA1C | ZFP36L1 | UBC | AP3M2 | CEBPD | PGR | PSAP | HES4 |
| 31 | TRABD2A | SRGN | CTSC | F2R | LY6E | CD8B | S100A6 | LAT2 | S1PR5 | HMGB1 | TUBB | RGCC | UBB | BTG1 | AKAP12 | COL6A1 | GRN | MARCKSL1 |
| 32 | ANK3 | CH25H | MIR4435-2HG | SLAMF7 | PARP14 | GZMH | ELK3 | GZMB | CTSW | MCM7 | SGO2 | IL7R | AHSA1 | NOP53 | TIMP1 | COL6A2 | CD83 | TP53INP1 |
| 33 | SARAF | SPOCK2 | LINC02099 | CXCR4 | NT5C3A | ENTPD1 | EEF1A1 | LINC00996 | ABHD17A | FABP5 | H2AFZ | CRTAM | NEU1 | ZBTB16 | CST3 | EMX2 | CTSH | APBA2 |
| 34 | AQP3 | ZNRF1 | MAGEH1 | SH2D1A | DDX58 | SRGAP3 | KIT | PRF1 | KLF2 | UBE2S | JPT1 | KDM6B | DNAJB6 | ERAP2 | NR2F1 | PALLD | GLUL | TSHR |
| 35 | RIPOR2 | CHN1 | SPOCK2 | FAM102A | IRF7 | VCAM1 | LTC4S | KLRF1 | PTPN12 | GAPDH | CEP55 | NR4A3 | GADD45B | CCND3 | DLK1 | PPP1R14A | MEF2C | NREP |
| 36 | AP3M2 | TNFRSF25 | PHACTR2 | CD52 | RNF213 | ID2 | RUNX2 | NCAM1 | TTC38 | CXCL13 | NUF2 | ATF3 | KLF6 | EEF1D | SERPING1 | RBP1 | CYBB | MME |
| 37 | ZFAS1 | CD200 | CARD16 | YBX3 | DDX60L | HLA-DRA | RORC | CXXC5 | KLRB1 | RANBP1 | KIF14 | DDX3X | BTG2 | PPP1R15A | BEX3 | C7 | CTSB | AC002454.1 |
| 38 | LINC02273 | METTL8 | S100A4 | STK17A | DDX60 | ITGAE | ZBTB16 | SH2D1B | CCL4 | H2AFX | KNL1 | DUSP6 | JUNB | PLK3 | C11orf96 | MFGE8 | CSF3R | CHI3L2 |
| 39 | EIF4B | RILPL2 | STAM | CCR5 | SAT1 | DUSP4 | FAM241A | MCTP2 | PTGDR | MCM3 | CENPA | GZMK | TNF | ZFP36L2 | FILIP1L | KANK2 | CD14 | SMIM3 |
| 40 | TOB1 | TNFRSF18 | GLRX | GPR174 | PPM1K | LYST | PDE4D | IFITM3 | ITGB2 | EZH2 | CDK1 | KLRG1 | ERN1 | EEF2 | RARRES1 | NR2F1 | C1QC | SSBP2 |
| 41 | SESN3 | SLA | SPATS2L | COTL1 | PNPT1 | TNFSF4 | IL23R | ZNF683 | XBP1 | TUBB4B | HMGB3 | JUNB | JUND | HNRNPA1 | COL1A2 | MFAP4 | SGK1 | UHRF1 |
| 42 | NSA2 | SMCO4 | AC005224.3 | CCL4L2 | PARP9 | NDFIP2 | B3GALT2 | ITGA1 | CEP78 | H2AFV | CDCA8 | RASGEF1B | ATF3 | VSIR | NUPR1 | COL1A1 | SPI1 | LRRC28 |
| 43 | PASK | BTLA | MAF | CD3E | IFIH1 | AKAP5 | CERK | IFITM2 | ARL4C | CDK1 | PLK1 | ANXA1 | DEDD2 | PASK | TCEAL9 | PBX1 | FCGRT | BCL11A |
| 44 | TNFRSF25 | NAP1L4 | PBXIP1 | TUBA4A | SP110 | GOLIM4 | TLE1 | CCL5 | BIN2 | HIST1H1D | AURKA | MCL1 | CD69 | EIF3H | MARCKSL1 | GSN | EGR1 | SCAI |
| 45 | FAU | FYB1 | F5 | PECAM1 | OAS2 | CD27 | EEF1B2 | ITGAX | LITAF | HNRNPAB | TROAP | CXCR4 | CLK1 | TXNIP | SLC40A1 | PDGFRB | FCGR2A | ATP6AP1L |
| 46 | EEF1D | MIR155HG | SLAMF1 | ARAP2 | C19orf66 | SNAP47 | CFH | CD38 | TRDC | CENPE | H2AFV | IER5 | IER5L | LDLRAP1 | CFH | SERPINF1 | PLAUR | RUFY3 |
| 47 | LDLRAP1 | PTPN13 | BTG3 | ITGA1 | STAT2 | RGS1 | PERP | SAMD3 | TXK | TK1 | KIF2C | MYADM | H3F3B | CMTM8 | APOE | SFRP1 | CPVL | CD79A |
| 48 | CTSL | SESN3 | TRAC | JAML | LAG3 | HLA-DPA1 | PLAT | SLC16A3 | MYOM2 | ZWINT | MAD2L1 | CD8A | NR4A1 | SCML1 | GNG11 | LHFPL6 | MS4A7 | GNA15 |
| 49 | ITGA6 | BIRC3 | IL1R1 | CD84 | LAP3 | LINC02446 | KIF5C | CAPN12 | GZMM | BIRC5 | NUCKS1 | PTGER4 | CXCR4 | LINC00402 | CDKN1C | PLAC9 | ALDH2 | HHIP-AS1 |
| 50 | PFDN5 | TP53INP1 | DNPH1 | AOAH | APOL6 | SAMSN1 | PLCB1 | CD247 | CD300A | PTTG1 | RAD21 | ZFP36L2 | EIF4A2 | RIPK2 | NBL1 | SERPING1 | SERPINA1 | GSTM3 |
enframe(sort(table(seu_obj$cluster_label))) %>%
mutate(name = ordered(name, levels = rev(name))) %>%
ggplot() +
geom_bar(aes(name, value), stat = "identity") +
theme(axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5)) +
labs(y = c("#cells"), x = "cluster")
alpha_lvl <- ifelse(nrow(plot_data) < 20000, 0.2, 0.1)
pt_size <- ifelse(nrow(plot_data) < 20000, 0.2, 0.05)
common_layers_disc <- list(
geom_point(size = pt_size, alpha = alpha_lvl),
NoAxes(),
guides(color = guide_legend(override.aes = list(size = 2, alpha = 1))),
labs(color = "")
)
common_layers_cont <- list(
geom_point(size = pt_size, alpha = alpha_lvl),
NoAxes(),
scale_color_gradientn(colors = viridis(9)),
guides(color = guide_colorbar())
)
ggplot(plot_data, aes(umapharmony_1, umapharmony_2, color = cluster_label)) +
common_layers_disc +
#facet_wrap(~therapy) +
ggtitle("Sub cluster")
my_subtypes <- names(clrs$cluster_label[[coi]])
my_subtypes <- c(my_subtypes, unlist(lapply(paste0("_", 1:3), function(x) paste0(my_subtypes, x)))) %>% .[!str_detect(., "doublet|dissociated")]
my_subtypes <- my_subtypes[my_subtypes %in% unique(seu_obj$cluster_label)]
my_subtypes <- my_subtypes[my_subtypes %in% names(clrs$cluster_label[[coi]])]
cells_to_keep <- colnames(seu_obj)[seu_obj$cluster_label %in% my_subtypes]
# seu_obj_sub <- subset(seu_obj, cells = cells_to_keep)
# seu_obj_sub <- RunUMAP(seu_obj_sub, dims = 1:50, reduction = "harmony", reduction.name = "umapharmony", reduction.key = "umapharmony_")
# seu_obj_sub$cluster_label <- seu_obj$cluster_label[colnames(seu_obj) %in% colnames(seu_obj_sub)]
# write_rds(seu_obj_sub, paste0("/work/shah/uhlitzf/data/SPECTRUM/freeze/v6/", coi, "_processed_filtered.rds"))
seu_obj_sub <- read_rds(paste0("/work/shah/uhlitzf/data/SPECTRUM/freeze/v6/", coi, "_processed_filtered.rds"))
plot_data_sub <- as_tibble(FetchData(seu_obj_sub, c(myfeatures, "cluster_label"))) %>%
left_join(meta_tbl, by = "sample") %>%
mutate(patient_id = str_remove_all(patient_id, "SPECTRUM-OV-"),
tumor_supersite = ordered(tumor_supersite, levels = names(clrs$tumor_supersite))) %>%
mutate(cell_id = colnames(seu_obj_sub),
cluster_label = ordered(cluster_label, levels = my_subtypes),
)
if (cell_sort == "CD45+") {
plot_data_sub <- filter(plot_data_sub, sort_parameters != "singlet, live, CD45-", !is.na(tumor_supersite))
}
if (cell_sort == "CD45-") {
plot_data_sub <- filter(plot_data_sub, sort_parameters != "singlet, live, CD45+", !is.na(tumor_supersite))
}
ggplot(plot_data_sub, aes(umapharmony_1, umapharmony_2, color = cluster_label)) +
common_layers_disc +
ggtitle("Sub cluster") +
#facet_wrap(~cluster_label) +
scale_color_manual(values = clrs$cluster_label[[coi]])
ggplot(plot_data_sub, aes(umapharmony_1, umapharmony_2, color = patient_id_short)) +
common_layers_disc +
ggtitle("Patient") +
# facet_wrap(~therapy) +
scale_color_manual(values = clrs$patient_id_short)
ggplot(plot_data_sub, aes(umapharmony_1, umapharmony_2, color = tumor_supersite)) +
# geom_point(aes(umapharmony_1, umapharmony_2),
# color = "grey90", size = 0.01,
# data = select(plot_data_sub, -tumor_supersite)) +
common_layers_disc +
ggtitle("Site") +
# facet_wrap(~therapy) +
scale_color_manual(values = clrs$tumor_supersite)
write_tsv(select(plot_data_sub, cell_id, everything(), -umapharmony_1, -umapharmony_2, -contains("RNA_")), paste0("/work/shah/uhlitzf/data/SPECTRUM/freeze/v6/", coi, "_embedding.tsv"))
big_clusters <- list(
CD8.T.ISG = c("CD8.T.ISG")
)
cells_to_keep_list <- lapply(big_clusters, function(x) colnames(seu_obj_sub)[seu_obj_sub$cluster_label %in% x])
# seu_list <- lapply(cells_to_keep_list, function(x) subset(seu_obj_sub, cells = x))
#
# preprocess_wrapper <- . %>%
# FindNeighbors(reduction = "harmony", dims = 1:50) %>%
# FindClusters(res = 0.1)
#
# seu_list <- lapply(seu_list, preprocess_wrapper)
# write_rds(seu_list, paste0("/work/shah/uhlitzf/data/SPECTRUM/freeze/v6/", coi, "_processed_filtered_sub_clusters.rds"))
seu_list <- read_rds(paste0("/work/shah/uhlitzf/data/SPECTRUM/freeze/v6/", coi, "_processed_filtered_sub_clusters.rds"))
cluster_vector_list <- list()
for (i in names(seu_list)) {
cluster_vector_list[[i]] <- cbind(cell_id = colnames(seu_list[[i]]), FetchData(seu_list[[i]], c("RNA_snn_res.0.1"))) %>%
as_tibble %>%
mutate(RNA_snn_res.0.1 = paste0(i, "_", RNA_snn_res.0.1)) %>%
deframe
}
cluster_vector <- unlist(cluster_vector_list, use.names = F) %>% setNames(lapply(cluster_vector_list, names) %>% unlist(use.names = F))
seu_obj$cluster_extended <- cluster_vector[seu_obj$cell_id] %>%
setNames(seu_obj$cell_id)
seu_obj_sub$cluster_extended <- cluster_vector[seu_obj_sub$cell_id] %>%
setNames(seu_obj_sub$cell_id)
cluster_extended_uniq <- as.character(na.omit(unique(seu_obj_sub$cluster_extended)))
Idents(seu_obj_sub) <- seu_obj_sub$cluster_extended
Idents(seu_obj) <- seu_obj$cluster_extended
# marker_tbl_extended <- lapply(
# cluster_extended_uniq,
# function(x) FindMarkers(seu_obj_sub, ident.1 = x)
# ) %>%
# setNames(cluster_extended_uniq) %>%
# bind_rows(.id = "cluster_extended") %>%
# as_tibble(rownames = "gene") %>%
# separate(gene, into = c("gene", "drop"), sep = "\\.\\.\\.") %>%
# select(-drop)
#
# write_tsv(marker_tbl_extended, paste0("/work/shah/uhlitzf/data/SPECTRUM/freeze/v6/", coi, "_marker_table_annotated_extended.tsv"))
marker_tbl_extended <- read_tsv(paste0("/work/shah/uhlitzf/data/SPECTRUM/freeze/v6/", coi, "_marker_table_annotated_extended.tsv"))
cluster_label_extended <- c(
CD8.T.ISG_0 = "CD8.T.ISG",
CD8.T.ISG_1 = "CD4.T.ISG",
CD8.T.ISG_2 = "dissociated_3"
)
names(seu_obj$cluster_label) <- colnames(seu_obj)
seu_obj$cluster_label[is.na(seu_obj$cluster_label)] <- "NA"
Idents(seu_obj) <- seu_obj$cluster_label
seu_obj$cluster_label[seu_obj$cluster_label == "CD8.T.ISG"] <- cluster_label_extended[seu_obj$cluster_extended][seu_obj$cluster_label == "CD8.T.ISG"]
Idents(seu_obj_sub) <- seu_obj_sub$cluster_label
seu_obj_sub$cluster_label[seu_obj_sub$cluster_label == "CD8.T.ISG"] <- cluster_label_extended[seu_obj_sub$cluster_extended][seu_obj_sub$cluster_label == "CD8.T.ISG"]
marker_sheet_extended <- marker_tbl_extended %>%
mutate(cluster_label = cluster_label_extended[cluster_extended]) %>%
group_by(cluster_label) %>%
mutate(rank = row_number()) %>%
slice(1:50) %>%
select(rank, gene, cluster_label) %>%
spread(cluster_label, gene)
marker_sheet_joined <- marker_sheet %>%
select(-CD8.T.ISG) %>%
left_join(marker_sheet_extended, by = "rank") %>%
gather(cluster_label, gene, -rank) %>%
mutate(cluster_label = ordered(cluster_label, levels = unique(c(names(clrs$cluster_label[[coi]]), sort(cluster_label))))) %>%
spread(cluster_label, gene)
cluster_n_tbl_full <- seu_obj$cluster_label %>%
table() %>%
enframe("cluster_label", "cluster_n") %>%
mutate(cluster_nrel = cluster_n/sum(cluster_n)) %>%
filter(cluster_label != "NA")
marker_tbl_extended_annotated <- marker_tbl_extended %>%
mutate(cluster_label = cluster_label_extended[cluster_extended]) %>%
select(-cluster_extended) %>%
left_join(cluster_n_tbl_full, by = "cluster_label")
marker_tbl_annotated_full <- marker_tbl_annotated %>%
filter(!(cluster_label %in% unlist(big_clusters))) %>%
bind_rows(marker_tbl_extended_annotated) %>%
mutate(cluster_label = ordered(cluster_label, levels = unique(c(names(clrs$cluster_label[[coi]]), sort(cluster_label))))) %>%
arrange(cluster_label, -avg_logFC)
# Idents(seu_obj_sub) <- seu_obj_sub$cluster_label
#
# cells_to_keep <- colnames(seu_obj_sub)[!(str_detect(seu_obj_sub$cluster_label, "dissociated|doublet"))]
# seu_obj_sub_sub <- subset(seu_obj_sub, cells = cells_to_keep)
# seu_obj_sub_sub <- RunUMAP(seu_obj_sub_sub, dims = 1:50, reduction = "harmony", reduction.name = "umapharmony", reduction.key = "umapharmony_")
# write_rds(seu_obj_sub_sub, paste0("/work/shah/uhlitzf/data/SPECTRUM/freeze/v6/", coi, "_processed_filtered_sub.rds"))
seu_obj_sub_sub <- read_rds(paste0("/work/shah/uhlitzf/data/SPECTRUM/freeze/v6/", coi, "_processed_filtered_sub.rds"))
formattable::formattable(marker_sheet_joined)
| rank | CD4.T.naive | CD4.T.ISG | CD4.T.CXCL13 | CD4.T.reg | CD8.T.cytotoxic | CD8.T.ISG | CD8.T.CXCL13 | NK.naive | NK.CD56 | NK.cytotoxic | Cycling.T.NK | Cycling.T.NK_1 | dissociated | dissociated_1 | dissociated_2 | dissociated_3 | doublet.Fibroblast | doublet.Fibroblast_1 | doublet.Monocyte | doublet.Plasma.cell |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | IL7R | IFIT3 | CXCL13 | TNFRSF4 | GZMK | IFIT3 | CXCL13 | KLRB1 | GNLY | FGFBP2 | STMN1 | CENPF | CCL4L2 | HSPA1A | KLF2 | XIST | DCN | IGFBP5 | HLA-DRA | SOX4 |
| 2 | CCR7 | ISG15 | NMB | IL2RA | CD8A | ISG15 | GZMB | IL4I1 | TYROBP | FCGR3A | TUBA1B | ASPM | CCL4 | HSPA1B | CCR7 | MALAT1 | IGFBP5 | TAGLN | CST3 | PTCRA |
| 3 | KLF2 | IFIT2 | NR3C1 | FOXP3 | CD8B | IFIT1 | CCL4L2 | IL7R | AREG | SPON2 | MKI67 | MKI67 | IFNG | MT1X | JUNB | NEAT1 | RBP1 | ADIRF | CXCL8 | MAL |
| 4 | EEF1B2 | MX2 | MAF | CTLA4 | ITM2C | MX1 | MIR155HG | LTB | KLRC1 | PRF1 | HIST1H4C | HMGB2 | FOS | DNAJB1 | FOS | MT-ND3 | C7 | DCN | SPP1 | MZB1 |
| 5 | TPT1 | IFI44L | FKBP5 | LTB | GZMH | IFIT2 | TNFRSF9 | LST1 | FCER1G | KLRF1 | TUBB | TOP2A | FOSB | MT1E | DUSP1 | MT-ND1 | MEG3 | IGFBP7 | S100A9 | DNTT |
| 6 | EEF1A1 | RSAD2 | IL6ST | RTKN2 | CCL5 | RSAD2 | HAVCR2 | TNFSF13B | TRDC | GNLY | TOP2A | UBE2C | TNF | HSPA6 | SELL | MT-CO3 | CALD1 | SPARCL1 | S100A8 | TFDP2 |
| 7 | MAL | IFIT1 | ITM2A | BATF | TRGC2 | IFI6 | RBPJ | CCR6 | XCL1 | KLRD1 | TYMS | CCNB1 | JUN | HSP90AA1 | IL7R | MT-ND2 | IGFBP4 | CALD1 | HLA-DQA1 | CD1E |
| 8 | TCF7 | MX1 | TSHZ2 | TNFRSF18 | KLRG1 | ISG20 | LAG3 | CTSH | KLRD1 | NKG7 | CENPF | UBE2S | EGR1 | HSPH1 | AREG | MT-CYB | RARRES2 | MGP | CD74 | STMN1 |
| 9 | CD40LG | IL7R | CTLA4 | SAT1 | GZMA | MX2 | IFNG | AQP3 | KRT81 | CX3CR1 | HMGB2 | PTTG1 | CCL3L1 | HSPE1 | EEF1A1 | MT-ATP6 | NR2F2 | C11orf96 | LYZ | ARPP21 |
| 10 | SELL | IFI6 | CD40LG | TBC1D4 | CCL4 | SAMD9L | CCL3 | CCL20 | XCL2 | GZMB | ASPM | TPX2 | NFKBID | HSPD1 | CD69 | MT-CO1 | SELENOP | MYL9 | FTL | AC084033.3 |
| 11 | GPR183 | TNFSF10 | PDCD1 | TIGIT | CRTAM | IFI44L | PTMS | NFKBIA | IGFBP2 | PLAC8 | NUSAP1 | STMN1 | CD69 | HSPB1 | BTG2 | PTPRC | MDK | TIMP3 | APOE | CDK6 |
| 12 | LDHB | STAT1 | CD4 | GADD45A | CST7 | HERC5 | CD8A | RORA | CLIC3 | CLIC3 | PCLAF | KPNA2 | NR4A2 | HSP90AB1 | GPR183 | MT-CO2 | SOX4 | MDK | HLA-DQB1 | MAP1A |
| 13 | NOSIP | HERC5 | LIMS1 | TNFRSF1B | GZMM | OAS1 | CRTAM | TNFRSF25 | IL2RB | PLEK | HMGN2 | CENPE | AC020916.1 | DNAJA1 | PIK3IP1 | H3F3B | ADIRF | TPM2 | MARCKS | AC011893.1 |
| 14 | SNHG8 | ISG20 | TNFRSF4 | PMAIP1 | HLA-DPB1 | MT2A | PHLDA1 | CEBPD | CEBPD | TYROBP | H2AFZ | CKS2 | EGR2 | JUN | CD55 | CCNI | MGP | NR2F2 | AIF1 | GLUL |
| 15 | PABPC1 | OAS1 | RNF19A | UGP2 | DTHD1 | STAT1 | FABP5 | TNFAIP3 | KRT86 | PTGDS | PCNA | TUBA1B | DUSP1 | CACYBP | DNAJB1 | GMFG | EGR1 | FBLN1 | C1QB | ADA |
| 16 | NOP53 | OAS3 | CORO1B | IKZF2 | PPP1R14B | TNFSF10 | TIGIT | NCR3 | TXK | EFHD2 | HIST1H1B | HMMR | TNFSF9 | HSPA8 | FKBP5 | COX5B | STAR | IGF1 | FTH1 | AL357060.1 |
| 17 | LEF1 | EIF2AK2 | RBPJ | TNFRSF9 | CD3G | OASL | KRT86 | SLC4A10 | CTSW | FCER1G | DUT | DLGAP5 | KLF6 | FKBP4 | TSC22D3 | CALM2 | SFRP4 | RAMP1 | BASP1 | GRASP |
| 18 | EIF3E | IFI44 | CPM | ICOS | THEMIS | PLSCR1 | JAML | TMIGD2 | KLRB1 | CST7 | CLSPN | CCNB2 | TNFAIP3 | CHORDC1 | RACK1 | PSMB1 | CLU | SELENOP | C1QA | CD1B |
| 19 | LTB | GBP1 | ZBED2 | LINC01943 | DUSP2 | GBP1 | CCL5 | DPP4 | MATK | GZMH | SMC4 | TUBB4B | DUSP2 | RGS2 | LDHB | TPM3 | ADAMTS1 | RARRES2 | C15orf48 | MIR181A1HG |
| 20 | RACK1 | CCR7 | AC004585.1 | IL32 | CD3D | EIF2AK2 | LINC01871 | TPT1 | CCL3 | ADGRG1 | ATAD2 | NUSAP1 | BTG2 | FOS | SARAF | AES | TIMP2 | LUM | FN1 | CCDC26 |
| 21 | NACA | EPSTI1 | TOX2 | SOX4 | LYAR | LAG3 | CXCR6 | MYBL1 | CD7 | CCL3 | SMC2 | BIRC5 | IER2 | ANXA1 | CXCR4 | ATP5MPL | TCEAL4 | IGFBP6 | MNDA | JCHAIN |
| 22 | UBA52 | MT2A | DUSP4 | ARID5B | TC2N | SAMD9 | TNIP3 | S100A4 | NKG7 | HOPX | TPX2 | HMGN2 | PPP1R15A | DNAJB4 | PLAC8 | GTF3A | C1R | CARMN | G0S2 | VIPR2 |
| 23 | TOMM7 | SAMD9L | AHI1 | CD27 | EOMES | IFI35 | PDCD1 | CD40LG | CD63 | IGFBP7 | TMPO | ARL6IP1 | NR4A1 | PPP1R15A | EEF1B2 | SAP18 | WFDC2 | IGFBP4 | APOC1 | ID1 |
| 24 | SOCS3 | XAF1 | ICA1 | BIRC3 | CXCR6 | EPSTI1 | HLA-DRB1 | SPOCK2 | HOPX | ZEB2 | HELLS | CDC20 | NFKBIA | ZFAND2A | TCF7 | GNG5 | FHL2 | DST | SOD2 | SOCS2 |
| 25 | JUNB | TMEM123 | ARID5B | LAYN | HLA-DPA1 | OAS3 | GAPDH | LINC01871 | TMIGD2 | PRSS23 | UBE2C | CDKN3 | JUND | FOSB | TPT1 | COPE | IFITM3 | CAV1 | NPC2 | RCAN1 |
| 26 | SERINC5 | DDX58 | CD84 | CORO1B | HLA-DRB1 | XAF1 | FAM3C | ERN1 | CMC1 | AKR1C3 | NASP | SMC4 | GADD45B | DNAJA4 | FOSB | TRMT112 | PEG3 | ID4 | LST1 | CLDN5 |
| 27 | TMEM123 | CMPK2 | CCDC50 | TYMP | SLF1 | USP18 | CTLA4 | JAML | TNFRSF18 | CD247 | DEK | CKS1B | TSC22D3 | DUSP1 | NOSIP | CIB1 | C1S | NUPR1 | GSN | CASC15 |
| 28 | EEF2 | CD40LG | IGFL2 | DUSP4 | APOBEC3G | LY6E | SPRY1 | FKBP11 | KLRC2 | MYBL1 | RRM2 | KIF20B | TAGAP | TSPYL2 | SC5D | RBX1 | LUM | CSRP2 | MS4A6A | PFKFB2 |
| 29 | FXYD5 | SAMD9 | RGS1 | CD4 | PPP2R5C | CMPK2 | CCND2 | IFNGR1 | SRGAP3 | AREG | CKS1B | GTSE1 | ZFP36 | SERPINH1 | NACA | BRK1 | SERPINF1 | CDKN1C | IL1B | GALNT2 |
| 30 | TSHZ2 | GPR183 | BATF | ENTPD1 | KIAA1551 | IFI44 | CD63 | MGAT4A | GSTP1 | C1orf21 | KNL1 | TUBA1C | ZFP36L1 | UBC | AP3M2 | HSPA8 | CEBPD | PGR | PSAP | HES4 |
| 31 | TRABD2A | USP18 | SRGN | CTSC | F2R | NT5C3A | CD8B | S100A6 | LAT2 | S1PR5 | HMGB1 | TUBB | RGCC | UBB | BTG1 | VAMP8 | AKAP12 | COL6A1 | GRN | MARCKSL1 |
| 32 | ANK3 | LY6E | CH25H | MIR4435-2HG | SLAMF7 | HELZ2 | GZMH | ELK3 | GZMB | CTSW | MCM7 | SGO2 | IL7R | AHSA1 | NOP53 | PSMB9 | TIMP1 | COL6A2 | CD83 | TP53INP1 |
| 33 | SARAF | DDX60L | SPOCK2 | LINC02099 | CXCR4 | IRF7 | ENTPD1 | EEF1A1 | LINC00996 | ABHD17A | FABP5 | H2AFZ | CRTAM | NEU1 | ZBTB16 | S100A11 | CST3 | EMX2 | CTSH | APBA2 |
| 34 | AQP3 | TRIM22 | ZNRF1 | MAGEH1 | SH2D1A | CD38 | SRGAP3 | KIT | PRF1 | KLF2 | UBE2S | JPT1 | KDM6B | DNAJB6 | ERAP2 | POMP | NR2F1 | PALLD | GLUL | TSHR |
| 35 | RIPOR2 | HELZ2 | CHN1 | SPOCK2 | FAM102A | TRIM22 | VCAM1 | LTC4S | KLRF1 | PTPN12 | GAPDH | CEP55 | NR4A3 | GADD45B | CCND3 | EDF1 | DLK1 | PPP1R14A | MEF2C | NREP |
| 36 | AP3M2 | ANXA1 | TNFRSF25 | PHACTR2 | CD52 | DDX58 | ID2 | RUNX2 | NCAM1 | TTC38 | CXCL13 | NUF2 | ATF3 | KLF6 | EEF1D | NDUFS5 | SERPING1 | RBP1 | CYBB | MME |
| 37 | ZFAS1 | NT5C3A | CD200 | CARD16 | YBX3 | PARP14 | HLA-DRA | RORC | CXXC5 | KLRB1 | RANBP1 | KIF14 | DDX3X | BTG2 | PPP1R15A | UQCR11 | BEX3 | C7 | CTSB | AC002454.1 |
| 38 | LINC02273 | IFIH1 | METTL8 | S100A4 | STK17A | CD8A | ITGAE | ZBTB16 | SH2D1B | CCL4 | H2AFX | KNL1 | DUSP6 | JUNB | PLK3 | HNRNPA1 | C11orf96 | MFGE8 | CSF3R | CHI3L2 |
| 39 | EIF4B | PLSCR1 | RILPL2 | STAM | CCR5 | DDX60 | DUSP4 | FAM241A | MCTP2 | PTGDR | MCM3 | CENPA | GZMK | TNF | ZFP36L2 | LDHB | FILIP1L | KANK2 | CD14 | SMIM3 |
| 40 | TOB1 | IFI35 | TNFRSF18 | GLRX | GPR174 | PPM1K | LYST | PDE4D | IFITM3 | ITGB2 | EZH2 | CDK1 | KLRG1 | ERN1 | EEF2 | ABRACL | RARRES1 | NR2F1 | C1QC | SSBP2 |
| 41 | SESN3 | IRF7 | SLA | SPATS2L | COTL1 | RNF213 | TNFSF4 | IL23R | ZNF683 | XBP1 | TUBB4B | HMGB3 | JUNB | JUND | HNRNPA1 | ATP5F1D | COL1A2 | MFAP4 | SGK1 | UHRF1 |
| 42 | NSA2 | DDX60 | SMCO4 | AC005224.3 | CCL4L2 | IFIH1 | NDFIP2 | B3GALT2 | ITGA1 | CEP78 | H2AFV | CDCA8 | RASGEF1B | ATF3 | VSIR | ANP32B | NUPR1 | COL1A1 | SPI1 | LRRC28 |
| 43 | PASK | PNPT1 | BTLA | MAF | CD3E | DDX60L | AKAP5 | CERK | IFITM2 | ARL4C | CDK1 | PLK1 | ANXA1 | DEDD2 | PASK | TRAPPC1 | TCEAL9 | PBX1 | FCGRT | BCL11A |
| 44 | TNFRSF25 | OAS2 | NAP1L4 | PBXIP1 | TUBA4A | OAS2 | GOLIM4 | TLE1 | CCL5 | BIN2 | HIST1H1D | AURKA | MCL1 | CD69 | EIF3H | SNRPB | MARCKSL1 | GSN | EGR1 | SCAI |
| 45 | FAU | PPM1K | FYB1 | F5 | PECAM1 | PARP9 | CD27 | EEF1B2 | ITGAX | LITAF | HNRNPAB | TROAP | CXCR4 | CLK1 | TXNIP | SEPT7 | SLC40A1 | PDGFRB | FCGR2A | ATP6AP1L |
| 46 | EEF1D | PARP9 | MIR155HG | SLAMF1 | ARAP2 | PNPT1 | SNAP47 | CFH | CD38 | TRDC | CENPE | H2AFV | IER5 | IER5L | LDLRAP1 | ZFAS1 | CFH | SERPINF1 | PLAUR | RUFY3 |
| 47 | LDLRAP1 | LGALS3BP | PTPN13 | BTG3 | ITGA1 | GZMK | RGS1 | PERP | SAMD3 | TXK | TK1 | KIF2C | MYADM | H3F3B | CMTM8 | SEC61G | APOE | SFRP1 | CPVL | CD79A |
| 48 | CTSL | LTB | SESN3 | TRAC | JAML | SP110 | HLA-DPA1 | PLAT | SLC16A3 | MYOM2 | ZWINT | MAD2L1 | CD8A | NR4A1 | SCML1 | UQCRH | GNG11 | LHFPL6 | MS4A7 | GNA15 |
| 49 | ITGA6 | PGAP1 | BIRC3 | IL1R1 | CD84 | BST2 | LINC02446 | KIF5C | CAPN12 | GZMM | BIRC5 | NUCKS1 | PTGER4 | CXCR4 | LINC00402 | PPP1CA | CDKN1C | PLAC9 | ALDH2 | HHIP-AS1 |
| 50 | PFDN5 | LAMP3 | TP53INP1 | DNPH1 | AOAH | C19orf66 | SAMSN1 | PLCB1 | CD247 | CD300A | PTTG1 | RAD21 | ZFP36L2 | EIF4A2 | RIPK2 | SUB1 | NBL1 | SERPING1 | SERPINA1 | GSTM3 |
write_tsv(marker_sheet_joined, paste0("/work/shah/uhlitzf/data/SPECTRUM/freeze/v6/", coi, "_marker_sheet_full.tsv"))
write_tsv(marker_sheet_joined, paste0("/work/shah/uhlitzf/data/SPECTRUM/freeze/v6/supplementary_tables/", coi, "_marker_sheet_full.tsv"))
write_tsv(marker_tbl_annotated_full, paste0("/work/shah/uhlitzf/data/SPECTRUM/freeze/v6/", coi, "_marker_table_annotated_full.tsv"))
write_tsv(marker_tbl_annotated_full, paste0("/work/shah/uhlitzf/data/SPECTRUM/freeze/v6/supplementary_tables/", coi, "_marker_table_annotated_full.tsv"))
plot_data_sub_sub <- as_tibble(FetchData(seu_obj_sub_sub, c(myfeatures, "cluster_label"))) %>%
left_join(meta_tbl, by = "sample") %>%
mutate(patient_id = str_remove_all(patient_id, "SPECTRUM-OV-"),
tumor_supersite = ordered(tumor_supersite, levels = names(clrs$tumor_supersite))) %>%
mutate(cell_id = colnames(seu_obj_sub_sub),
cluster_label = ordered(cluster_label, levels = names(clrs$cluster_label[[coi]])),
)
if (cell_sort == "CD45+") {
plot_data_sub_sub <- filter(plot_data_sub_sub, sort_parameters != "singlet, live, CD45-", !is.na(tumor_supersite))
}
if (cell_sort == "CD45-") {
plot_data_sub_sub <- filter(plot_data_sub_sub, sort_parameters != "singlet, live, CD45+", !is.na(tumor_supersite))
}
ggplot(plot_data_sub_sub, aes(umapharmony_1, umapharmony_2, color = cluster_label)) +
common_layers_disc +
ggtitle("Sub cluster") +
#facet_wrap(~cluster_label) +
scale_color_manual(values = clrs$cluster_label[[coi]])
signature_modules <- read_excel("_data/small/signatures/SPECTRUM v5 sub cluster markers.xlsx", sheet = 2, skip = 1, range = "M2:P100") %>%
gather(module, gene) %>%
na.omit() %>%
group_by(module) %>%
do(gene = c(.$gene)) %>%
{setNames(.$gene, .$module)}
signature_modules$ISG.module <- c("CCL5", "CXCL10", "IFNA1", "IFNB1", "ISG15", "IFI27L2", "SAMD9L")
## compute expression module scores
for (i in 1:length(signature_modules)) {
seu_obj_sub_sub <- AddModuleScore(seu_obj_sub_sub, features = signature_modules[i], name = names(signature_modules)[i])
seu_obj_sub_sub[[names(signature_modules)[i]]] <- seu_obj_sub_sub[[paste0(names(signature_modules)[i], "1")]]
seu_obj_sub_sub[[paste0(names(signature_modules)[i], "1")]] <- NULL
print(paste(names(signature_modules)[i], "DONE"))
}
## [1] "CD8.Cytotoxic DONE"
## [1] "CD8.Dysfunctional DONE"
## [1] "CD8.Naive DONE"
## [1] "CD8.Predysfunctional DONE"
## [1] "ISG.module DONE"
## compute progeny scores
progeny_list <- seu_obj_sub_sub@assays$RNA@data[VariableFeatures(seu_obj_sub_sub),] %>%
as.matrix %>%
progeny %>%
as.data.frame %>%
as.list
names(progeny_list) <- make.names(paste0(names(progeny_list), ".pathway"))
for (i in 1:length(progeny_list)) {
seu_obj_sub_sub <- AddMetaData(seu_obj_sub_sub,
metadata = progeny_list[[i]],
col.name = names(progeny_list)[i])
}
write_rds(seu_obj_sub_sub, paste0("/work/shah/uhlitzf/data/SPECTRUM/freeze/v6/", coi, "_processed_filtered_annotated.rds"))
seu_obj_sub_sub <- read_rds(paste0("/work/shah/uhlitzf/data/SPECTRUM/freeze/v6/", coi, "_processed_filtered_annotated.rds"))
marker_top_tbl <- marker_sheet_joined[,-1] %>%
slice(1:10) %>%
as.list %>%
.[!str_detect(names(.), "doublet|dissociated")] %>%
enframe("cluster_label_x", "gene") %>%
unnest(gene)
plot_data_markers <- as_tibble(FetchData(seu_obj_sub_sub, c("cluster_label", myfeatures, unique(marker_top_tbl$gene)))) %>%
gather(gene, value, -c(1:(length(myfeatures)+1))) %>%
left_join(meta_tbl, by = "sample") %>%
mutate(patient_id = str_remove_all(patient_id, "SPECTRUM-OV-"),
tumor_supersite = ordered(tumor_supersite, levels = names(clrs$tumor_supersite))) %>%
mutate(cluster_label = ordered(cluster_label, levels = my_subtypes)) %>%
group_by(cluster_label, gene) %>%
summarise(value = mean(value, na.rm = T)) %>%
group_by(gene) %>%
mutate(value = scales::rescale(value)) %>%
left_join(marker_top_tbl, by = "gene") %>%
mutate(cluster_label_x = ordered(cluster_label_x, levels = rev(names(clrs$cluster_label[[coi]]))))
ggplot(plot_data_markers) +
geom_tile(aes(gene, cluster_label, fill = value)) +
theme(axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5)) +
facet_grid(~cluster_label_x, scales = "free", space = "free") +
scale_fill_gradientn(colors = viridis(9)) +
labs(fill = "Scaled\nexpression") +
theme(aspect.ratio = 1,
axis.line = element_blank(),
axis.ticks = element_blank(),
axis.title = element_blank())
# ggsave(paste0("_fig/002_marker_heatmap_", coi, ".pdf"), width = nrow(marker_top_tbl)/6, height = 5)
comp_site_tbl <- plot_data_sub_sub %>%
filter(!is.na(tumor_supersite)) %>%
group_by(cluster_label, tumor_supersite) %>%
tally %>%
group_by(tumor_supersite) %>%
mutate(nrel = n/sum(n)*100) %>%
ungroup
pnrel_site <- ggplot(comp_site_tbl) +
geom_bar(aes(tumor_supersite, nrel, fill = cluster_label),
stat = "identity", position = position_stack()) +
scale_y_continuous(expand = c(0, 0)) +
theme(axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5)) +
labs(fill = "Cluster", y = "Fraction [%]", x = "") +
scale_fill_manual(values = clrs$cluster_label[[coi]])
pnabs_site <- ggplot(comp_site_tbl) +
geom_bar(aes(tumor_supersite, n, fill = cluster_label),
stat = "identity", position = position_stack()) +
scale_y_continuous(expand = c(0, 0)) +
theme(axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5)) +
labs(fill = "Cluster", y = "# cells", x = "") +
scale_fill_manual(values = clrs$cluster_label[[coi]])
plot_grid(pnabs_site, pnrel_site, ncol = 2, align = "h")
# ggsave(paste0("_fig/02_deep_dive_", coi, "_comp_site.pdf"), width = 8, height = 4)
comp_tbl_sample_sort <- plot_data_sub_sub %>%
group_by(tumor_subsite, tumor_supersite, patient_id, consensus_signature, therapy, cluster_label) %>%
tally %>%
group_by(tumor_subsite, tumor_supersite, patient_id, consensus_signature, therapy) %>%
mutate(nrel = n/sum(n)*100,
nsum = sum(n),
log10n = log10(n),
label_supersite = "Site",
label_mutsig = "Signature",
label_therapy = "Rx") %>%
ungroup %>%
arrange(desc(therapy), tumor_supersite) %>%
mutate(tumor_subsite_rx = paste0(tumor_subsite, "_", therapy)) %>%
mutate(tumor_subsite = ordered(tumor_subsite, levels = unique(tumor_subsite)),
tumor_subsite_rx = ordered(tumor_subsite_rx, levels = unique(tumor_subsite_rx))) %>%
arrange(patient_id) %>%
mutate(label_patient_id = ifelse(as.logical(as.numeric(fct_inorder(as.character(patient_id)))%%2), "Patient1", "Patient2"))
sample_id_x_tbl <- plot_data_sub %>%
mutate(sort_short_x = cell_sort) %>%
distinct(patient_id, sort_short_x, tumor_subsite, therapy, sample) %>%
unite(sample_id_x, patient_id, sort_short_x, tumor_subsite, therapy) %>%
arrange(sample_id_x)
comp_tbl_sample_sort %>%
mutate(sort_short_x = cell_sort) %>%
unite(sample_id_x, patient_id, sort_short_x, tumor_subsite_rx) %>%
select(sample_id_x, cluster_label, n, nrel, nsum) %>%
left_join(sample_id_x_tbl, by = "sample_id_x") %>%
write_tsv(paste0("/work/shah/uhlitzf/data/SPECTRUM/freeze/v6/", coi, "_subtype_compositions.tsv"))
ybreaks <- c(500, 1000, 2000, 4000, 6000, 8000, 10000, 15000, 20000)
max_cells_per_sample <- max(comp_tbl_sample_sort$nsum)
ymaxn <- ybreaks[max_cells_per_sample < ybreaks][1]
comp_plot_wrapper <- function(y = "nrel", switch = NULL) {
if (y == "nrel") ylab <- paste0("Fraction\nof cells [%]")
if (y == "n") ylab <- paste0("Number\nof cells")
p <- ggplot(comp_tbl_sample_sort,
aes_string("tumor_subsite_rx", y, fill = "cluster_label")) +
facet_grid(~patient_id, space = "free", scales = "free", switch = switch) +
coord_cartesian(clip = "off") +
scale_fill_manual(values = clrs$cluster_label[[coi]]) +
theme(axis.text.x = element_blank(),
axis.title.y = element_text(angle = 0, vjust = 0.5, hjust = 0.5,
margin = margin(0, -0.4, 0, 0, unit = "npc")),
axis.ticks.x = element_blank(),
axis.title.x = element_blank(),
axis.line.x = element_blank(),
strip.text.y = element_blank(),
strip.text.x = element_blank(),
strip.background.y = element_blank(),
strip.background.x = element_blank(),
plot.margin = margin(0, 0, 0, 0, "npc")) +
labs(x = "",
y = ylab) +
guides(fill = FALSE)
if (y == "nrel") p <- p +
geom_bar(stat = "identity") +
scale_y_continuous(expand = c(0, 0),
breaks = c(0, 50, 100),
labels = c("0", "50", "100"))
if (y == "n") p <- p +
geom_bar(stat = "identity", position = position_stack()) +
scale_y_continuous(expand = c(0, 0),
breaks = c(0, ymaxn/2, ymaxn),
limits = c(0, ymaxn),
labels = c("", ymaxn/2, ymaxn)) +
expand_limits(y = c(0, ymaxn)) +
theme(panel.grid.major.y = element_line(linetype = 1, color = "grey90", size = 0.5))
return(p)
}
common_label_layers <- list(
geom_tile(color = "white", size = 0.15),
theme(axis.text.x = element_blank(),
axis.ticks = element_blank(),
axis.title.x = element_blank(),
axis.line.x = element_blank(),
strip.text = element_blank(),
strip.background = element_blank(),
plot.margin = margin(0, 0, 0, 0, "npc")),
scale_y_discrete(expand = c(0, 0)),
labs(y = ""),
guides(fill = FALSE),
facet_grid(~patient_id,
space = "free", scales = "free")
)
comp_label_site <- ggplot(distinct(comp_tbl_sample_sort, tumor_subsite_rx, therapy, tumor_supersite, label_supersite, patient_id),
aes(tumor_subsite_rx, label_supersite,
fill = tumor_supersite)) +
scale_fill_manual(values = clrs$tumor_supersite) +
common_label_layers
comp_label_rx <- ggplot(distinct(comp_tbl_sample_sort, tumor_subsite_rx, therapy, tumor_supersite, label_therapy, consensus_signature, patient_id),
aes(tumor_subsite_rx, label_therapy,
fill = therapy)) +
scale_fill_manual(values = c(`post-Rx` = "gold3", `pre-Rx` = "steelblue")) +
common_label_layers
comp_label_mutsig <- ggplot(distinct(comp_tbl_sample_sort, tumor_subsite_rx, therapy, tumor_supersite, label_mutsig, consensus_signature, patient_id),
aes(tumor_subsite_rx, label_mutsig,
fill = consensus_signature)) +
scale_fill_manual(values = clrs$consensus_signature) +
common_label_layers
patient_label_tbl <- distinct(comp_tbl_sample_sort, patient_id, .keep_all = T)
comp_label_patient_id <- ggplot(patient_label_tbl, aes(tumor_subsite_rx, label_patient_id)) +
scale_fill_manual(values = clrs$consensus_signature) +
geom_text(aes(tumor_subsite_rx, label_patient_id, label = patient_id)) +
facet_grid(~patient_id,
space = "free", scales = "free") +
coord_cartesian(clip = "off") +
theme_void() +
theme(strip.text = element_blank(),
strip.background = element_blank(),
plot.margin = margin(0, 0, 0, 0, "npc"))
hist_plot_wrapper <- function(x = "nrel") {
if (x == "nrel") {
xlab <- paste0("Fraction of cells [%]")
bw <- 5
}
if (x == "log10n") {
xlab <- paste0("Number of cells")
bw <- 0.2
}
p <- ggplot(comp_tbl_sample_sort) +
ggridges::geom_density_ridges(
aes_string(x, "cluster_label", fill = "cluster_label"), color = "black",
stat = "binline", binwidth = bw, scale = 3) +
facet_grid(label_supersite~.,
space = "free", scales = "free") +
scale_fill_manual(values = clrs$cluster_label[[coi]]) +
theme(axis.text.y = element_blank(),
axis.ticks.y = element_blank(),
axis.title.y = element_blank(),
axis.line.y = element_blank(),
strip.text = element_blank(),
strip.background = element_blank(),
plot.margin = margin(0, 0, 0, 0, "npc")) +
scale_x_continuous(expand = c(0, 0)) +
scale_y_discrete(expand = c(0, 0)) +
guides(fill = FALSE) +
labs(x = xlab)
if (x == "log10n") p <- p + expand_limits(x = c(0, 3)) +
scale_x_continuous(expand = c(0, 0),
labels = function(x) parse(text = paste("10^", x)))
return(p)
}
pcomp1 <- comp_plot_wrapper("n")
pcomp2 <- comp_plot_wrapper("nrel")
pcomp_grid <- plot_grid(comp_label_patient_id,
pcomp1, pcomp2,
comp_label_site, comp_label_rx, comp_label_mutsig,
ncol = 1, align = "v",
rel_heights = c(0.15, 0.33, 0.33, 0.06, 0.06, 0.06))
phist1 <- hist_plot_wrapper("log10n")
pcomp_hist_grid <- ggdraw() +
draw_plot(pcomp_grid, x = 0.01, y = 0, width = 0.85, height = 1) +
draw_plot(phist1, x = 0.87, y = 0.05, width = 0.12, height = 0.8)
pcomp_hist_grid
# ggsave(paste0("_fig/02_composition_v6_",coi,".pdf"), pcomp_hist_grid, width = 10, height = 2)
comp_tbl_z <- comp_tbl_sample_sort %>%
filter(therapy == "pre-Rx",
!(tumor_supersite %in% c("Ascites", "Other"))) %>%
group_by(patient_id, cluster_label) %>%
arrange(patient_id, cluster_label, nrel) %>%
mutate(rank = row_number(nrel),
z_rank = scales::rescale(rank)) %>%
mutate(mean_nrel = mean(nrel, na.rm = T),
sd_nrel = sd(nrel, na.rm = T),
z_nrel = (nrel - mean_nrel) / sd_nrel) %>%
ungroup()
ggplot(comp_tbl_z) +
geom_boxplot(aes(tumor_supersite, z_nrel, color = tumor_supersite),
outlier.shape = NA) +
geom_point(aes(tumor_supersite, z_nrel, color = tumor_supersite), position = position_jitter(), size = 0.1) +
facet_grid(~cluster_label, scales = "free_x", space = "free_x") +
theme(axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5),
aspect.ratio = 5) +
scale_color_manual(values = clrs$tumor_supersite)
ggplot(comp_tbl_z) +
geom_boxplot(aes(tumor_supersite, z_rank, color = tumor_supersite),
outlier.shape = NA) +
geom_point(aes(tumor_supersite, z_rank, color = tumor_supersite), position = position_jitter(), size = 0.1) +
facet_grid(~cluster_label, scales = "free_x", space = "free_x") +
theme(axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5),
aspect.ratio = 5) +
scale_color_manual(values = clrs$tumor_supersite)
## sub cluster CD8 cells
# seu_obj_cd8 <- seu_obj_sub_sub %>%
# subset(subset = cluster_label %in% my_subtypes[str_detect(my_subtypes, "CD8.T")]) %>%
# RunUMAP(dims = 1:50, reduction = "harmony", reduction.name = "umapharmony", reduction.key = "umapharmony_")
#
# write_rds(seu_obj_cd8, "/work/shah/uhlitzf/data/SPECTRUM/freeze/v6/CD8.T_processed.rds")
seu_obj_cd8 <- read_rds("/work/shah/uhlitzf/data/SPECTRUM/freeze/v6/CD8.T_processed.rds")
# marker_tbl <- FindAllMarkers(seu_obj_cd8, only.pos = T)
# write_tsv(as_tibble(marker_tbl), "/work/shah/uhlitzf/data/SPECTRUM/freeze/v6/CD8.T_markers.tsv")
# marker_tbl <- read_tsv("/work/shah/uhlitzf/data/SPECTRUM/freeze/v6/CD8.T_markers.tsv")
#
# ## Hypergeometric test --------------------------------------
#
# test_set <- marker_tbl %>%
# group_by(cluster) %>%
# filter(!str_detect(gene, "^RPS|^RPL")) %>%
# slice(1:50) %>%
# mutate(k = length(cluster)) %>%
# ungroup %>%
# select(cluster, gene, k) %>%
# mutate(join_helper = 1) %>%
# group_by(cluster, join_helper, k) %>%
# nest(test_set = gene)
#
# markers_doub_tbl <- markers_v6 %>%
# enframe("subtype", "gene") %>%
# filter(!(subtype %in% unique(c(coi, cell_type_major)))) %>%
# unnest(gene) %>%
# group_by(gene) %>%
# filter(length(gene) == 1) %>%
# mutate(subtype = paste0("doublet.", subtype)) %>%
# bind_rows(tibble(subtype = "Mito.high", gene = grep("^MT-", rownames(seu_obj), value = T)))
#
# ref_set <- markers_v6_super[["CD8.T"]] %>%
# bind_rows(markers_doub_tbl) %>%
# group_by(subtype) %>%
# mutate(m = length(gene),
# n = length(rownames(seu_obj))-m,
# join_helper = 1) %>%
# group_by(subtype, m, n, join_helper) %>%
# nest(ref_set = gene)
#
# hyper_tbl <- test_set %>%
# left_join(ref_set, by = "join_helper") %>%
# group_by(cluster, subtype, m, n, k) %>%
# do(q = length(intersect(unlist(.$ref_set), unlist(.$test_set)))) %>%
# mutate(pval = 1-phyper(q = q, m = m, n = n, k = k)) %>%
# ungroup %>%
# mutate(qval = p.adjust(pval, "BH"),
# sig = qval < 0.01)
#
# # hyper_tbl %>%
# # group_by(subtype) %>%
# # filter(any(qval < 0.01)) %>%
# # ggplot(aes(subtype, -log10(qval), fill = sig)) +
# # geom_bar(stat = "identity") +
# # facet_wrap(~cluster) +
# # coord_flip()
#
# low_rank <- str_detect(unique(hyper_tbl$subtype), "Mito|doublet")
# subtype_lvl <- c(sort(unique(hyper_tbl$subtype)[!low_rank]), sort(unique(hyper_tbl$subtype)[low_rank]))
#
# cluster_label_tbl <- hyper_tbl %>%
# mutate(subtype = ordered(subtype, levels = subtype_lvl)) %>%
# arrange(qval, subtype) %>%
# group_by(cluster) %>%
# slice(1) %>%
# mutate(subtype = ifelse(sig, as.character(subtype), paste0("unknown_", cluster))) %>%
# select(cluster, cluster_label = subtype) %>%
# ungroup %>%
# mutate(cluster_label = make.unique(cluster_label, sep = "_"))
#
# marker_sheet <- marker_tbl %>%
# left_join(cluster_label_tbl, by = "cluster") %>%
# group_by(cluster_label) %>%
# filter(!str_detect(gene, "^RPS|^RPL")) %>%
# slice(1:50) %>%
# mutate(rank = row_number()) %>%
# select(cluster_label, gene, rank) %>%
# spread(cluster_label, gene) %>%
# mutate_all(.funs = helper_f)
#
# formattable::formattable(marker_sheet)
# write_tsv(marker_sheet, paste0("/work/shah/uhlitzf/data/SPECTRUM/freeze/v6/CD8.T.cell_marker_sheet.tsv"))
# seu_obj_cd8$cluster_label <- unname(deframe(cluster_label_tbl)[as.character(unlist(seu_obj_cd8[[paste0("RNA_snn_res.", louvain_resolution)]]))])
#
# seu_obj_sub_sub <- seu_obj_sub
#
# cluster_label_tbl_1 <- as_tibble(cbind(cell_id=colnames(seu_obj_sub), FetchData(seu_obj_sub, c("cluster_label"))))
# cluster_label_tbl_2 <- as_tibble(cbind(cell_id=colnames(seu_obj_cd8), FetchData(seu_obj_cd8, c("cluster_label"))))
#
# cluster_label_tbl <- left_join(cluster_label_tbl_1, cluster_label_tbl_2, by = "cell_id") %>%
# mutate(cluster_label = ifelse(is.na(cluster_label.y), cluster_label.x, cluster_label.y))
#
# seu_obj_sub_sub$cluster_label <- cluster_label_tbl$cluster_label
#
# seu_obj_sub_sub <- subset(seu_obj_sub_sub, subset = cluster_label != "doublet.Plasma.cell")
# write_rds(seu_obj_sub_sub, "/work/shah/uhlitzf/data/SPECTRUM/freeze/v6/T.cell_processed_filtered_sub.rds")
# root_cell <- "SPECTRUM-OV-036_S1_CD45P_PELVIC_PERITONEUM_CGGACACCAACGACTT"
# seu_obj_cd8_sub <- subset(seu_obj_cd8, subset = cluster_label != "doublet.Plasma.cell" & cluster_label != "gd.T.cell")
# seu_obj_cd8_sub <- subset(seu_obj_cd8_sub, cells = c(root_cell, colnames(seu_obj_cd8_sub)[colnames(seu_obj_cd8_sub)!=root_cell][-1]))
#
# dc_obj <- DiffusionMap(seu_obj_cd8_sub@reductions$harmony@cell.embeddings, k = 100)
# dc_mat <- dc_obj@eigenvectors
# colnames(dc_mat) <- paste0("DC_", 1:ncol(dc_mat))
# seu_obj_cd8_sub[["DC"]] <- CreateDimReducObject(embeddings = dc_mat, key = "DC_", assay = DefaultAssay(seu_obj_cd8_sub))
#
# write_rds(seu_obj_cd8_sub, "/work/shah/uhlitzf/data/SPECTRUM/freeze/v6/CD8.T_processed_filtered.rds")
#
# dpt_obj <- DPT(dc_obj)
# write_rds(dpt_obj, "/work/shah/uhlitzf/data/SPECTRUM/freeze/v6/CD8.T_sub_dpt.rds")
# seu_obj_cd8_sub$DPT1 <- dpt_obj$DPT1
#
# write_rds(seu_obj_cd8_sub, "/work/shah/uhlitzf/data/SPECTRUM/freeze/v6/CD8.T_processed_filtered.rds")
seu_obj_cd8_sub <- read_rds("/work/shah/uhlitzf/data/SPECTRUM/freeze/v6/CD8.T_processed_filtered.rds")
# plot_data_sub_sub <- as_tibble(FetchData(seu_obj_sub_sub, c(myfeatures, "cluster_label"))) %>%
# left_join(meta_tbl, by = "sample") %>%
# mutate(patient_id = str_remove_all(patient_id, "SPECTRUM-OV-"),
# tumor_supersite = ordered(tumor_supersite, levels = names(clrs$tumor_supersite))) %>%
# mutate(cell_id = colnames(seu_obj_sub_sub),
# cluster_label = ordered(cluster_label, levels = my_subtypes),
# )
#
# if (cell_sort == "CD45+") {
# plot_data_sub_sub <- filter(plot_data_sub_sub, sort_parameters != "singlet, live, CD45-", !is.na(tumor_supersite))
# }
#
# if (cell_sort == "CD45-") {
# plot_data_sub_sub <- filter(plot_data_sub_sub, sort_parameters != "singlet, live, CD45+", !is.na(tumor_supersite))
# }
#
# ggplot(plot_data_sub_sub, aes(umapharmony_1, umapharmony_2, color = cluster_label)) +
# common_layers_disc +
# ggtitle("Sub cluster") +
# #facet_wrap(~cluster_label) +
# scale_color_manual(values = clrs$cluster_label[[coi]])
#
# ggplot(plot_data_sub_sub, aes(umapharmony_1, umapharmony_2, color = patient_id_short)) +
# common_layers_disc +
# ggtitle("Patient") +
# # facet_wrap(~therapy) +
# scale_color_manual(values = clrs$patient_id_short)
#
# ggplot(plot_data_sub_sub, aes(umapharmony_1, umapharmony_2, color = tumor_supersite)) +
# # geom_point(aes(umapharmony_1, umapharmony_2),
# # color = "grey90", size = 0.01,
# # data = select(plot_data_sub_sub, -tumor_supersite)) +
# common_layers_disc +
# ggtitle("Site") +
# # facet_wrap(~therapy) +
# scale_color_manual(values = clrs$tumor_supersite)
#
# write_tsv(select(plot_data_sub_sub, cell_id, everything(), -umapharmony_1, -umapharmony_2, -contains("RNA_")), paste0("/work/shah/uhlitzf/data/SPECTRUM/freeze/v6/", coi, "_embedding_sub.tsv"))
plot_data_cd8_sub <- as_tibble(FetchData(seu_obj_cd8_sub, c(myfeatures, "cluster_label", "DC_1", "DC_2"))) %>%
left_join(meta_tbl, by = "sample") %>%
mutate(patient_id = str_remove_all(patient_id, "SPECTRUM-OV-"),
tumor_supersite = ordered(tumor_supersite, levels = names(clrs$tumor_supersite))) %>%
mutate(cell_id = colnames(seu_obj_cd8_sub),
cluster_label = ordered(cluster_label, levels = my_subtypes),
)
if (cell_sort == "CD45+") {
plot_data_cd8_sub <- filter(plot_data_cd8_sub, sort_parameters != "singlet, live, CD45-", !is.na(tumor_supersite))
}
if (cell_sort == "CD45-") {
plot_data_cd8_sub <- filter(plot_data_cd8_sub, sort_parameters != "singlet, live, CD45+", !is.na(tumor_supersite))
}
ggplot(plot_data_cd8_sub, aes(umapharmony_1, umapharmony_2, color = cluster_label)) +
common_layers_disc +
ggtitle("Sub cluster") +
#facet_wrap(~cluster_label) +
scale_color_manual(values = clrs$cluster_label[[coi]])
ggplot(plot_data_cd8_sub, aes(umapharmony_1, umapharmony_2, color = patient_id_short)) +
common_layers_disc +
ggtitle("Patient") +
# facet_wrap(~therapy) +
scale_color_manual(values = clrs$patient_id_short)
ggplot(plot_data_cd8_sub, aes(umapharmony_1, umapharmony_2, color = tumor_supersite)) +
common_layers_disc +
ggtitle("Site") +
# facet_wrap(~therapy) +
scale_color_manual(values = clrs$tumor_supersite)
ggplot(plot_data_cd8_sub, aes(DC_1, DC_2, color = cluster_label)) +
common_layers_disc +
ggtitle("Sub cluster") +
#facet_wrap(~cluster_label) +
scale_color_manual(values = clrs$cluster_label[[coi]])
ggplot(plot_data_cd8_sub, aes(DC_1, DC_2, color = patient_id_short)) +
common_layers_disc +
ggtitle("Patient") +
# facet_wrap(~therapy) +
scale_color_manual(values = clrs$patient_id_short)
ggplot(plot_data_cd8_sub, aes(DC_1, DC_2, color = tumor_supersite)) +
common_layers_disc +
ggtitle("Site") +
# facet_wrap(~therapy) +
scale_color_manual(values = clrs$tumor_supersite)
devtools::session_info()
## ─ Session info ───────────────────────────────────────────────────────────────
## setting value
## version R version 3.6.2 (2019-12-12)
## os Debian GNU/Linux 10 (buster)
## system x86_64, linux-gnu
## ui X11
## language (EN)
## collate en_US.UTF-8
## ctype en_US.UTF-8
## tz Etc/UTC
## date 2021-01-21
##
## ─ Packages ───────────────────────────────────────────────────────────────────
## package * version date lib
## abind 1.4-5 2016-07-21 [2]
## ape 5.3 2019-03-17 [2]
## assertthat 0.2.1 2019-03-21 [2]
## backports 1.1.10 2020-09-15 [1]
## bibtex 0.4.2.2 2020-01-02 [2]
## Biobase 2.46.0 2019-10-29 [2]
## BiocGenerics 0.32.0 2019-10-29 [2]
## BiocParallel 1.20.1 2019-12-21 [2]
## bitops 1.0-6 2013-08-17 [2]
## boot 1.3-24 2019-12-20 [3]
## broom 0.7.2 2020-10-20 [1]
## callr 3.4.2 2020-02-12 [1]
## car 3.0-8 2020-05-21 [1]
## carData 3.0-4 2020-05-22 [1]
## caTools 1.17.1.4 2020-01-13 [2]
## cellranger 1.1.0 2016-07-27 [2]
## class 7.3-15 2019-01-01 [3]
## cli 2.0.2 2020-02-28 [1]
## cluster 2.1.0 2019-06-19 [3]
## codetools 0.2-16 2018-12-24 [3]
## colorblindr * 0.1.0 2020-01-13 [2]
## colorspace * 1.4-2 2019-12-29 [2]
## cowplot * 1.0.0 2019-07-11 [2]
## crayon 1.3.4 2017-09-16 [1]
## curl 4.3 2019-12-02 [2]
## data.table 1.12.8 2019-12-09 [2]
## DBI 1.1.0 2019-12-15 [2]
## dbplyr 2.0.0 2020-11-03 [1]
## DelayedArray 0.12.2 2020-01-06 [2]
## DEoptimR 1.0-8 2016-11-19 [1]
## desc 1.2.0 2018-05-01 [2]
## destiny * 3.0.1 2020-01-16 [1]
## devtools 2.2.1 2019-09-24 [2]
## digest 0.6.25 2020-02-23 [1]
## dplyr * 1.0.2 2020-08-18 [1]
## e1071 1.7-3 2019-11-26 [1]
## ellipsis 0.3.1 2020-05-15 [1]
## evaluate 0.14 2019-05-28 [2]
## fansi 0.4.1 2020-01-08 [2]
## farver 2.0.3 2020-01-16 [1]
## fitdistrplus 1.0-14 2019-01-23 [2]
## forcats * 0.5.0 2020-03-01 [1]
## foreign 0.8-74 2019-12-26 [3]
## formattable 0.2.0.1 2016-08-05 [1]
## fs 1.5.0 2020-07-31 [1]
## future 1.15.1 2019-11-25 [2]
## future.apply 1.4.0 2020-01-07 [2]
## gbRd 0.4-11 2012-10-01 [2]
## gdata 2.18.0 2017-06-06 [2]
## generics 0.0.2 2018-11-29 [2]
## GenomeInfoDb 1.22.0 2019-10-29 [2]
## GenomeInfoDbData 1.2.2 2020-01-14 [2]
## GenomicRanges 1.38.0 2019-10-29 [2]
## ggplot.multistats 1.0.0 2019-10-28 [1]
## ggplot2 * 3.3.2 2020-06-19 [1]
## ggrepel 0.8.1 2019-05-07 [2]
## ggridges 0.5.2 2020-01-12 [2]
## ggthemes 4.2.0 2019-05-13 [1]
## globals 0.12.5 2019-12-07 [2]
## glue 1.3.2 2020-03-12 [1]
## gplots 3.0.1.2 2020-01-11 [2]
## gridExtra 2.3 2017-09-09 [2]
## gtable 0.3.0 2019-03-25 [2]
## gtools 3.8.1 2018-06-26 [2]
## haven 2.3.1 2020-06-01 [1]
## hexbin 1.28.0 2019-11-11 [2]
## hms 0.5.3 2020-01-08 [1]
## htmltools 0.4.0 2019-10-04 [2]
## htmlwidgets 1.5.1 2019-10-08 [2]
## httr 1.4.2 2020-07-20 [1]
## ica 1.0-2 2018-05-24 [2]
## igraph 1.2.5 2020-03-19 [1]
## IRanges 2.20.2 2020-01-13 [2]
## irlba 2.3.3 2019-02-05 [2]
## jsonlite 1.7.1 2020-09-07 [1]
## KernSmooth 2.23-16 2019-10-15 [3]
## knitr 1.26 2019-11-12 [2]
## labeling 0.3 2014-08-23 [2]
## laeken 0.5.1 2020-02-05 [1]
## lattice 0.20-38 2018-11-04 [3]
## lazyeval 0.2.2 2019-03-15 [2]
## leiden 0.3.1 2019-07-23 [2]
## lifecycle 0.2.0 2020-03-06 [1]
## listenv 0.8.0 2019-12-05 [2]
## lmtest 0.9-37 2019-04-30 [2]
## lsei 1.2-0 2017-10-23 [2]
## lubridate 1.7.9.2 2020-11-13 [1]
## magrittr * 2.0.1 2020-11-17 [1]
## MASS 7.3-51.5 2019-12-20 [3]
## Matrix 1.2-18 2019-11-27 [3]
## matrixStats 0.56.0 2020-03-13 [1]
## memoise 1.1.0 2017-04-21 [2]
## metap 1.2 2019-12-08 [2]
## mnormt 1.5-5 2016-10-15 [2]
## modelr 0.1.8 2020-05-19 [1]
## multcomp 1.4-12 2020-01-10 [2]
## multtest 2.42.0 2019-10-29 [2]
## munsell 0.5.0 2018-06-12 [2]
## mutoss 0.1-12 2017-12-04 [2]
## mvtnorm 1.0-12 2020-01-09 [2]
## nlme 3.1-143 2019-12-10 [3]
## nnet 7.3-12 2016-02-02 [3]
## npsurv 0.4-0 2017-10-14 [2]
## numDeriv 2016.8-1.1 2019-06-06 [2]
## openxlsx 4.1.5 2020-05-06 [1]
## pbapply 1.4-2 2019-08-31 [2]
## pcaMethods 1.78.0 2019-10-29 [2]
## pillar 1.4.6 2020-07-10 [1]
## pkgbuild 1.0.6 2019-10-09 [2]
## pkgconfig 2.0.3 2019-09-22 [1]
## pkgload 1.0.2 2018-10-29 [2]
## plotly 4.9.1 2019-11-07 [2]
## plotrix 3.7-7 2019-12-05 [2]
## plyr 1.8.5 2019-12-10 [2]
## png 0.1-7 2013-12-03 [2]
## prettyunits 1.1.1 2020-01-24 [1]
## processx 3.4.2 2020-02-09 [1]
## progeny * 1.11.3 2020-10-22 [1]
## proxy 0.4-24 2020-04-25 [1]
## ps 1.3.2 2020-02-13 [1]
## purrr * 0.3.4 2020-04-17 [1]
## R.methodsS3 1.7.1 2016-02-16 [2]
## R.oo 1.23.0 2019-11-03 [2]
## R.utils 2.9.2 2019-12-08 [2]
## R6 2.4.1 2019-11-12 [1]
## ranger 0.12.1 2020-01-10 [1]
## RANN 2.6.1 2019-01-08 [2]
## rappdirs 0.3.1 2016-03-28 [2]
## RColorBrewer 1.1-2 2014-12-07 [2]
## Rcpp 1.0.4 2020-03-17 [1]
## RcppAnnoy 0.0.16 2020-03-08 [1]
## RcppEigen 0.3.3.7.0 2019-11-16 [2]
## RcppHNSW 0.2.0 2019-09-20 [2]
## RcppParallel 4.4.4 2019-09-27 [2]
## RCurl 1.98-1.1 2020-01-19 [1]
## Rdpack 0.11-1 2019-12-14 [2]
## readr * 1.4.0 2020-10-05 [1]
## readxl * 1.3.1 2019-03-13 [2]
## rematch 1.0.1 2016-04-21 [2]
## remotes 2.1.0 2019-06-24 [2]
## reprex 0.3.0 2019-05-16 [2]
## reshape2 1.4.3 2017-12-11 [2]
## reticulate 1.14 2019-12-17 [2]
## rio 0.5.16 2018-11-26 [1]
## rlang 0.4.8 2020-10-08 [1]
## rmarkdown 2.0 2019-12-12 [2]
## robustbase 0.93-6 2020-03-23 [1]
## ROCR 1.0-7 2015-03-26 [2]
## rprojroot 1.3-2 2018-01-03 [2]
## RSpectra 0.16-0 2019-12-01 [2]
## rstudioapi 0.11 2020-02-07 [1]
## rsvd 1.0.3 2020-02-17 [1]
## Rtsne 0.15 2018-11-10 [2]
## rvest 0.3.6 2020-07-25 [1]
## S4Vectors 0.24.2 2020-01-13 [2]
## sandwich 2.5-1 2019-04-06 [2]
## scales 1.1.0 2019-11-18 [2]
## scatterplot3d 0.3-41 2018-03-14 [1]
## sctransform 0.2.1 2019-12-17 [2]
## SDMTools 1.1-221.2 2019-11-30 [2]
## sessioninfo 1.1.1 2018-11-05 [2]
## Seurat * 3.1.2 2019-12-12 [2]
## SingleCellExperiment 1.8.0 2019-10-29 [2]
## smoother 1.1 2015-04-16 [1]
## sn 1.5-4 2019-05-14 [2]
## sp 1.4-2 2020-05-20 [1]
## stringi 1.5.3 2020-09-09 [1]
## stringr * 1.4.0 2019-02-10 [1]
## SummarizedExperiment 1.16.1 2019-12-19 [2]
## survival 3.1-8 2019-12-03 [3]
## testthat 2.3.2 2020-03-02 [1]
## TFisher 0.2.0 2018-03-21 [2]
## TH.data 1.0-10 2019-01-21 [2]
## tibble * 3.0.4 2020-10-12 [1]
## tidyr * 1.1.2 2020-08-27 [1]
## tidyselect 1.1.0 2020-05-11 [1]
## tidyverse * 1.3.0 2019-11-21 [2]
## tsne 0.1-3 2016-07-15 [2]
## TTR 0.23-6 2019-12-15 [1]
## usethis 1.5.1 2019-07-04 [2]
## uwot 0.1.5 2019-12-04 [2]
## vcd 1.4-7 2020-04-02 [1]
## vctrs 0.3.5 2020-11-17 [1]
## VIM 6.0.0 2020-05-08 [1]
## viridis * 0.5.1 2018-03-29 [2]
## viridisLite * 0.3.0 2018-02-01 [2]
## withr 2.3.0 2020-09-22 [1]
## xfun 0.12 2020-01-13 [2]
## xml2 1.3.2 2020-04-23 [1]
## xts 0.12-0 2020-01-19 [1]
## XVector 0.26.0 2019-10-29 [2]
## yaml 2.2.1 2020-02-01 [1]
## zip 2.0.4 2019-09-01 [1]
## zlibbioc 1.32.0 2019-10-29 [2]
## zoo 1.8-7 2020-01-10 [2]
## source
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##
## [1] /home/uhlitzf/R/lib
## [2] /usr/local/lib/R/site-library
## [3] /usr/local/lib/R/library